Inquiry skills tutoring system

ABSTRACT

An assessment engine includes a definition of inquiry skills being assessed. Assessment models are used to infer skill demonstration as one or more students engage in inquiry within computerized simulations and/or microworlds. A pedagogical agent and/or help system provides real-time feedback to one or more students based on the assessment model outputs, and/or based on additional models that track one or more students developing proficiency across inquiry tasks over time. A pedagogical agent and/or help system for science inquiry tutoring responds in real-time on the basis of knowledge-engineered and data-mined assessment and/or tracking models.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/159,539, filed May 19, 2016, which is a continuation of U.S.application Ser. No. 14/167,215, filed Jan. 29, 2014, now U.S. Pat. No.9,373,082, issued Jun. 21, 2016, which claims the benefit of U.S.Provisional Application No. 61/759,668, filed on Feb. 1, 2013. Theentire teachings of the above applications are incorporated herein byreference.

GOVERNMENT SUPPORT

This invention was supported, in whole or in part, by contract NSF-DRL#1008649 from National Science Foundation. The government has certainrights in the invention.

BACKGROUND

Current course management systems, such as assessment systems or leaningenvironments, are designed for well-defined domains, i.e. those forwhich there are well-known, well-defined solutions to problems. Forexample, current systems assess students using multiple choice questions(e.g., Moodle), or simple equations (e.g., mathematics software such asthe ASSISTments System developed by Neil Heffernan at WorcesterPolytechnic Institute WPI). Learning systems are lacking in areasaddressing ill-defined domains, where solutions are not well-defined(e.g., design, abstract problem solving, or conducting scienceexperiments).

SUMMARY OF THE INVENTION

Embodiments of the present invention address the above shortcomings ofthe art. Inquiry tutoring systems and methods focus on assessing,tracking, and supporting one or more students' scientific inquiry skillsas they conduct inquiry in real-time within simulations and/ormicroworlds. The inquiry tutoring system leverages a pedagogical agentand/or help system capable of assessing what a student is doing as thestudent (or to any user, learner, or person that is engaged in any formof education or learning) conducts experiments and/or investigations,and may determine when and whether a student needs help. The agent isfurther configured to respond to the student in real-time to providehints and guidance about how to better conduct inquiry.

In one embodiment, a method, system, or program product comprises, in aprocessor, defining one or more measurable science inquiry skills orpractices. The method, system, or program product may further include,in a computer, measuring the one or more science inquiry skills of asubject person, the measuring being in real-time and using at least oneof an assessment model and a tracking model (an assessment model and/ora tracking model) programmed to infer (evaluate, assess, derive,measure, characterize) science inquiry skill demonstration frominteractive engagement by the subject person with a simulation and/ormicroworld environment comprised of the simulation and/or microworldand, optionally, other interfaces to elicit the subject person'sscientific inquiry. The measuring of the one or more science inquiryskills of a subject person may be performed by an assessment modeland/or a tracking model. The method, system, or program product mayfurther provide to the subject person real-time feedback through thesimulation environment, the real-time feedback being based on theassessment model, the tracking model, or both; and providing to thesubject person guidance on how to better conduct scientific inquiry.

In another embodiment, the assessment model and/or tracking modelevaluates and estimates proficiency at science inquiry of the subjectperson using different data-mining based algorithms. In anotherembodiment, the assessment model and/or tracking model evaluates andestimates proficiency at science inquiry of the subject person usingdifferent knowledge-engineering based algorithms. In another embodiment,the assessment model and/or tracking model evaluates and estimatesproficiency at science inquiry of the subject person using a combinationof different knowledge-engineering based algorithms and data-miningbased algorithms.

In another embodiment, the measuring of one or more science inquiryskills provides a performance assessment of at least one or moreaggregate science inquiry skills.

In another embodiment, the tracking model tracks the subject person'sdevelopment of the one or more science inquiry skills over time andacross one or more science topics or science domains, wherein thetracking utilizes one or more data-mining based models. In yet anotherembodiment, the tracking uses the one or more data-mining based modelsto aggregate information about the subject person and to provide atleast one measurement or evaluation of the proficiency for the subjectperson in one or more science inquiry skills.

In another embodiment, the tracking model tracks the subject person'sdevelopment of the one or more science inquiry skills over time andacross one or more science topics or science domains, wherein thetracking utilizes one or more knowledge-engineering based models. In yetanother embodiment, the tracking uses the one or moreknowledge-engineering based models to aggregate information about thesubject person and to provide at least one measurement or evaluation ofthe proficiency for the subject person in one or more science inquiryskills.

In another embodiment, the tracking model tracks the subject person'sdevelopment of the one or more science inquiry skills over time andacross one or more science topics or science domains, wherein thetracking utilizes both one or more data-mining based models andknowledge-engineering based models. In yet another embodiment, thetracking uses the one or more data-mining based models andknowledge-engineering based models to aggregate information about thesubject person and to provide at least one measurement or evaluation ofthe proficiency for the subject person in one or more science inquiryskills.

In another embodiment, the real-time feedback is based on at least oneof: a knowledge-engineering based assessment model, a data-mining basedassessment model, a knowledge-engineering based tracking model, and adata-mining based tracking model.

In another embodiment, the simulated scientific inquiry includes atleast one of: generating hypotheses, collecting data, interpreting thecollected data, warranting claims with data, and communicatingrespective findings. In another embodiment, providing real-time feedbackthrough the simulation environment includes providing multi-levelfeedback regarding at least one of orienting, organizational,procedural, conceptual, and instrumental aspects of the scientificinquiry. In yet another embodiment, the real-time assessment andfeedback may be generated based on a student's eye-tracking patternsduring the various phases of inquiry including the observation ofdata/phenomena. An embodiment may employ eye-tracking methods such as anInstruction System With Eyetracking-based Adaptive Scaffolding, in U.S.patent application Ser. No. 13/774,981, or other methods to detectwhether a subject person is actively observing data/phenomena in thesimulated scientific inquiry.

In another embodiment, the subject person includes one or more students.

In another embodiment, skills include at least at least one of or anysubskill of a data collection skill, a data interpretation skill, ahypothesis skill, a claim warranting skill, a communicating findingsskill, an identification of an independent variable skill, anidentification of a dependent variable skill, a defining of arelationship between variables skill, a designing a controlledexperiment skill, a testing a stated hypothesis skill, a warranting aninterpretation skill, and a relating an interpretation to a hypothesisskill.

The inquiry tutoring approach differs from current systems and providesseveral advantages. First, some embodiments focus specifically onassessment of inquiry subskills, culled from more general inquiry skillsdefined in national and state frameworks. The system measures theseskills/subskills within simulation and/or microworld environments, whichthus act as performance assessment of inquiry skills. Second, the systemassesses and scaffolds inquiry skills in real-time as students conducttheir investigations. As described in more detail below, the inquirytutoring system assesses defined inquiry skills/subskills using datamining-based and/or knowledge-engineering-based models. The system alsotracks the development of specific inquiry skills/subskills using datamining-based models and/or knowledge-engineering-based models. Thesystem also employs a pedagogical agent and/or help system to providefeedback to students driven by the data mining-based and/orknowledge-engineering-based assessment and tracking models.

A computer-implemented method for inquiry tutoring may include definingone or more measurable science inquiry skills/subskills comprisinggeneral science inquiry skills. For example, general science inquiryskills may include hypothesizing, experimenting, interpreting data,warranting claims, and communicating findings. Subskills may include,for example, but are not limited to, identifying an independentvariable, identifying a dependent variable, defining a relationshipbetween one or more variables, designing a controlled experiment,testing a stated hypothesis, warranting an interpretation, relating aninterpretation to a hypothesis, communicating findings, and/or the like.In one embodiment, the inquiry tutoring method may include measuring theone or more inquiry skills/subskills in a subject person. Measuringskills/subskills may occur in real-time and use an assessment engineprogrammed to infer (derive, measure, evaluate) inquiry skill/subskilldemonstrations from interactive engagement by the subject person with asimulation environment designed for scientific inquiry.

In other embodiments, the general science inquiry skills may be sciencepractices, general inquiry skills, or the like. Such skills include, butare not limited to the following: (a) asking questions, (b) developingworking models, (c) planning and carrying out investigations, (d)analyzing and interpreting data, (e) using mathematical andcomputational thinking, (f) constructing explanations, (g) engaging inargument from evidence, (h) obtaining, evaluating, and communicatinginformation.

In other embodiments, the general science inquiry skills may beengineering design practices, general engineering skills, or the like.Such skills include designing models for engineering, including but notlimited to environmental engineering, industrial engineering,biomechanical engineering, mechanical engineering, chemical engineering,software engineering, computer engineering, electrical engineering, orother types of engineering. General science skills may also includeengineering skills, such as engineering design practices. Subskills heremay include specifying a parameter for a design and testing theviability of that parameter using a controlled experiment, theninterpreting the viability of that parameter for the design.

Embodiments may provide to the subject person real-time feedback througha simulation and/or microworld environment, wherein real-time feedbackis based on output from an assessment engine, a tracking engine, orboth. The inquiry tutoring method may also provide feedback throughguidance on how to better conduct scientific inquiry. In one embodiment,an assessment engine estimates proficiency of the subject person usingdifferent data-mining based algorithms. A data mining algorithm mayinclude formulating data retrieval and organizing the retrieved data. Inone embodiment, an assessment engine may include knowledge-engineeredrules assessing performance of one or more science inquiryskills/subskills. In another embodiment the assessment engine may alsoinclude data-mined rules to assess performance of one or moreill-defined (domains in which there are multiple correct solutions orpaths) or well-defined (domains for which there are well-known,well-defined solutions to problems) science inquiry skills/subskills. Inyet another embodiment, the assessment engine may include bothknowledge-engineered rules and data-mined rules. In one embodiment, themeasuring of science inquiry skills/subskills provides a performanceassessment of science inquiry skills.

The inquiry tutoring method may also include tracking the subjectperson's development of the science inquiry skills/subskills over timeand across shared science domains. In one embodiment, tracking utilizesone or more data mining-based models. Tracking may use data-mining-basedmodels to aggregate information about the subject person and to provideestimates of whether the subject person knows the science inquiry skillsor not. In one embodiment, tracking utilizes one or moreknowledge-engineering-based models. In another embodiment, trackingutilizes one or more data-mining-based models and/orknowledge-engineering-based models.

In yet another embodiment, the inquiry tutoring method may include bothan assessment engine and a tracking engine. In one embodiment, theassessment engine and the tracking engine may includeknowledge-engineered based models. In another embodiment the assessmentengine and the tracking engine may also include data-mined rules toassess and track performance of one or more ill-defined science inquirysubskills. In yet another embodiment, the assessment engine and thetracking engine may include both knowledge-engineered rules anddata-mined rules.

The inquiry tutoring methods may include a simulated scientific inquiry.The simulated scientific inquiry may include the subject persongenerating hypotheses, collecting data to test the hypotheses,interpreting the collected data, warranting claims with data, andcommunicating his findings. In another embodiment, providing real-timefeedback through the simulation environment includes providingmulti-level feedback including, but not limited to, procedural,instrumental, orienting, organizational, and conceptual aspects of thescientific inquiry. The subject person using the inquiry tutoring methodmay include one or more users, e.g., one or more students, apprentices,trainees, groups, teams, and/or the like. In addition, the scienceinquiry subskills may include at least, for example, a hypothesizingskill, an experiment design skill, a data collection skill, a datainterpretation skill, a claim warranting skill, and a communicatingfindings skill.

Another embodiment of the present invention may include a system,including a processing module configured to define one or moremeasurable science inquiry subskills forming general science inquiryskills. The system may also include a computing module configured tomeasure, in real-time, the one or more science inquiry subskills of asubject person and use an assessment model and/or a tracking modelprogrammed to evaluate, measure, determine and/or track science inquiryskill/subskill demonstration from interactive engagement by the subjectperson with a simulation environment for scientific inquiry. The systemmay also include a user interface module configured to provide to thesubject person real-time feedback through the simulation environment,the real-time feedback being based on the assessment model. In oneembodiment, an assessment model may assess the subject using at leastone of a data-mining and/or knowledge-engineering based model. Inanother embodiment, a tracking model may track the subject using atleast one of a data-mining and/or knowledge-engineering based model. Inanother embodiment, an assessment model may assess and the trackingmodel may track the subject using data-mining or knowledge-engineeringbased models. In addition, the user interface module may be furtherconfigured to provide guidance to the subject person on how to betterconduct scientific inquiry.

In another embodiment of the system of the present invention, theassessment model and/or the tracking model may estimate and trackproficiency of science inquiry of the subject person using differentdata mining-based or knowledge-engineering-based algorithms. Theassessment models and/or the tracking models may be formed fromknowledge-engineered rules assessing performance of one or morewell-defined science inquiry skills/subskills, and the assessment and/ortracking models may be formed from data-mined rules to assessperformance of one or more ill-defined science inquiry skills/subskills.In the system, the computing module may provide a performance assessmentof the one or more science inquiry skills by measuring one or morescience inquiry skills. In the system, the assessment models and/ortracking model may further perform tracking of the subject person'sdevelopment of the one or more science inquiry skills/subskills overtime and across shared science domains, wherein the tracking utilizesone or more data mining-based models.

In another embodiment of the system of the present invention, theassessment model and/or the tracking model use the one or more datamining-based models to aggregate information about the subject personand to provide estimates of whether the subject person knows the one ormore science inquiry skills or not, and the real-time feedback isfurther based on the one or more data mining-based models and/orknowledge-engineering-based models from the tracking. In the system, thesimulated scientific inquiry includes the subject person generatinghypotheses, collecting data to test the hypotheses, interpreting thecollected data, warranting claims with data, and the subject personcommunicates respective findings. In the system, the user interfacemodule is further configured to provide real-time feedback through thesimulation environment by providing multi-level feedback regardingorienting, organizational, procedural, conceptual, and instrumentalaspects of the scientific inquiry such as, for example, a hypothesizingskill, an experiment design skill, a data collection skill, a datainterpretation skill, a claim warranting skill, and a communicatingfindings skill.

Another embodiment of the present invention may include a computerprogram product providing a tutoring agent. The computer program productmay include a non-transitory computer useable medium having a computerreadable program. The computer readable program when executed on acomputer may cause the computer to define one or more measurable scienceinquiry subskills underlying general science inquiry skills. The programmay also measure the one or more science inquiry subskills of a subjectperson, the measuring being in real-time and using an assessment modelprogrammed to infer, by which we mean evaluate, measure, etc. scienceinquiry subskill demonstration from interactive engagement by thesubject person with a simulation environment for scientific inquiry. Theprogram may also perform tracking of the subject person's development ofthe one or more science inquiry subskills over time and across sharedscience domains. The program may also provide to the subject personreal-time feedback through the simulation environment, the real-timefeedback being based on the assessment models. In addition, the programmay provide to the subject person guidance on how to better conductscientific inquiry.

In accordance with the above, in an example embodiment, the presentmethods, systems, and apparatuses comprise defining one or moremeasurable science inquiry subskills forming general science inquiryskills. A computer then measuring the one or more inquiry subskills of asubject person, said measuring being in-real time and using assessmentmodels programmed to infer inquiry subskill demonstration frominteractive engagement by the subject person with a simulationenvironment for scientific inquiry. The methods, systems, andapparatuses then provide to the subject person real-time feedback beingbased on the assessment models, and provide to the subject personguidance on how to better conduct scientific inquiry.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of embodiments, as illustrated in the accompanying drawings.The drawings are not necessarily to scale, emphasis instead being placedupon illustrating embodiments.

FIG. 1 is a block diagram illustrating an example embodiment of asoftware architecture of the present invention.

FIG. 2 is a flow diagram illustrating one embodiment of a method,system, or process for inquiry tutoring according to the presentinvention.

FIG. 3 is a block diagram illustrating one embodiment of an inquirytutoring method, system, or implementation according to the presentinvention.

FIGS. 4A-4E are screenviews of example embodiments of a simulated userinterface of the present invention.

FIG. 5 is a block diagram of a computer system for inquiry tutoringaccording to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

A description of embodiments follows. The teachings of all patents,published applications and references cited herein are incorporated byreference in their entirety.

As used herein, “infer” and “inferring” may include, but are not limitedto evaluating, assessing, deriving, measuring, characterizing a value ora set of values.

As used herein, “well-defined” may include, but are not limited todomains for which there are well-known, well-defined solutions toproblems.

As used herein, “μl-defined” may include, but are not limited to domainsfor there are multiple correct solutions or paths.

As used herein, “student” may include, but are not limited to any user,learner, or person that is engaged in any form of education or learning.

As used herein, an “estimate” may include, but is not limited tomeasurements based on metrics or indices obtained by the present methodsand systems.

Inquiry skills may provide an indication of a user's proficiency with atechnical area. Current systems do not adequately capture a user'sability to inquire because of the reliance on hand-scoring of dataand/or on multiple choice tests that do not represent and measureproficiencies of science inquiry skills. The inquiry tutoring system ofthe present invention defines a general set of skills and a specific setof skills. The inquiry tutoring system further defines, captures, andmaintains a set of educational log data for collection from one or moresimulation environments. Based on the data collected, the inquirytutoring system determines whether the educational log data indicates askill having reached a level of proficiency. From the proficiencydetermination, the inquiry tutoring system may provide real-timefeedback through a help system associated with the simulationenvironment, wherein real-time feedback is based on output fromassessment and/or tracking engine or models. In one embodiment, the helpsystem may be a pedagogical agent.

FIG. 1 is a block diagram illustrating an example embodiment of thepresent invention having a software architecture 100. The softwarearchitecture may include an assessment engine 110, a tracking component170, a help system 140, and a user interface 160.

The assessment engine 110 may include measurable skills 112, knowledgeengineered rules and models 114, and data mined rules and models 116.Measurable skills 112 may be culled from a more general or a specificset of inquiry skills defined in a framework, including, but not limitedto, a national or state framework. For example, general inquiry skillsmay include, but are not limited to, generating hypotheses, collectingdata to test the hypotheses, interpreting the collected data, warrantingclaims with data, or communicating respective findings. Specific inquiryskills may include, for example, identifying an independent variable,identifying a dependent variable, defining a relationship between avariable, designing a controlled experiment, testing a statedhypothesis, warranting an interpretation, relating an interpretation toa hypothesis, and/or the like. Specific inquiry skills may be referredto as sub skills. In one embodiment, the assessment engine 110 is incommunication with the tracking component 170, the help system 140, andthe user interface 160 over a network. The help system 140 may be apedagogical agent. The assessment engine 110 may include componentscommunicating with or among other components residing on or with otherengines or agents.

The assessment engine 110 may include a knowledge-engineered rule base114. The knowledge-engineered rule base 114 may include a hypothesisrule base, an experiment rule base, an analysis rule base, a warrantingclaims rule base, and/or the like. The assessment engine 110 may includedata-mining-based assessment algorithms, rules, and models 116. Adata-mining based assessment algorithm may include maintainingdata-mining information (e.g., educational log data, summary features ofthose log data, streaming data, data that lives in computer memory) andassessing the data-mining information. In one embodiment, a data-miningbased assessment (at 116) may include a decision tree with cutoff valuesfor specific features. The cutoff values may be used by the assessmentengine 110 to infer or predict whether the educational log dataindicates that a user demonstrates proficiency in one or more skills.The knowledge engineered rules and models 114 and the data-mining rulesand models 116 may assess the performance of one or more well-definedscience inquiry skills. The knowledge engineered rules and models 114and the data-mining rules and models 116 may assess the performance ofone or more ill-defined science inquiry skills.

The help system 140 may be in communication with user interface 160 orthrough one or more engines or components, e.g., the assessment engine110 and/or tracking component 170 as illustrated in FIG. 1. In oneembodiment, the pedagogical agent and/or help system 140 may include acomputer-based character providing feedback to users in the form ofmessages, such as, text, graphics, and multimedia. For example, theagent may respond, through one or more messages, in real-time, as usersare interacting with the user interface 160, simulation environment,and/or the like. The messages from the help system 140 may be driven bythe assessment engine 110, through knowledge-engineered 114 and/ordata-mined 116 assessment rules and models. In one embodiment, thepedagogical agent and/or help system 140 may provide feedback based ontracked user performance, user performance across different sciencedomains, and/or user performance over time. This is accomplished throughdata-mining based models 151 and/or knowledge-engineered based models153 described below. The assessment may also be real-time, where thehelp system 140 processes historical and/or real time educational logdata with real-time input, (e.g., responses from a user to messages fromthe pedagogical agent and/or help system 140).

As noted above, the pedagogical agent and/or help system 140 mayinteract with a user as the user conducts an inquiry within one or moresimulation environments. As the user is working within the simulationenvironment, the assessment engine 110 may conduct an assessment for agiven skill, e.g., skill A. Based on the assessment, the pedagogicalagent and/or help system 140 determines an appropriate feedback messageregarding skill A. As the student engages further with the simulationenvironment, the assessment engine 110 continues to evaluate/assess userproficiency for skill A and continues to conduct assessment of skill A,which may be provided to the help system 140 for determining a feedbackmessage to provide to the student.

The tracking component 170 may include measurable skills 117, datamining based models 151, and knowledge-engineered based models 153. Oneor more of the models 151, 153 may include one or more aggregate and/orestimate models. “Estimate” may include, but is not limited tomeasurements based on metrics or indices obtained by the present methodsand systems. The models 151, 153 may be searchable by query search orgraph search. For example, a query search may include formulatingkeywords and/or query statements, such as, SELECT, FROM, WHERE. Themodels 151 and 153 may also be graph searchable by skill, by topic, bystudent, and by grade. Data mining may be accomplished by combining(aggregating) results from query statements with graph searches. In oneembodiment, the tracking component 170 may track user progress over timeusing Bayesian knowledge tracing. For example, tracking user progressmay include storing educational log data for an individual student in adatabase record. Tracking may also include tracking progress for a classof students, students of a grade level, a school population, a district,a state, and/or a geographical region over time. As a student or userengages in several inquiry activities, the inquiry tutoring systemincludes data mined-based models 151 and knowledge-engineered basedmodels 153 that aggregate this information and provide estimates of thestudent's proficiency within the set of inquiry subskills. Thisinformation may be syndicated to the pedagogical agent and/or helpsystem 140 to utilize in determining one or more messages to display tothe user or may be syndicated to a teacher to provide formativeassessment feedback about student progress. In one embodiment, Bayesianknowledge tracing models populate the database record field(s)indicating proficiency estimates at a skill(s) (and thus inquiry skill)per student. Similarly, in response to the pedagogical agent and/or helpsystem 140 sharing a scaffolding message to the student, the assessmentengine 110 populates or otherwise indicates pertinent data in thedatabase record of the student. This enables subsequent reporting to ateacher of the student's progress per sub skill.

The user interface 160 may be in communication with the assessmentengine 110, tracking component 170 and/or pedagogical agent and/or helpsystem 140. In one embodiment, the user interface 160 may display thesimulation environment to the user with the pedagogical agent and/orhelp system 140 appearing when the help system decides a message shouldbe delivered using assessments generated by the assessment engine 110.In another embodiment, the pedagogical agent and/or help system 140 mayappear based on information generated from the tracking component 170.

FIG. 2 is a flow diagram illustrating an example embodiment of a processwhich occurs within the assessment engine 110 and/or the trackingcomponent 170 of FIG. 1. The flow diagram 200 may include receivingeducational log data 201. Educational log data 201 may includeinformation logged from one or more simulation environments. Theeducational log data 201 may be received and stored in an online manner,including but not limited to, receiving online data, or real timeprocessing of students' interaction data. The educational log data 201may be received and stored in an offline manner, including but notlimited to, storing data to a database or a file server. The educationallog data 201 may include, but is not limited to, log files, graphicaluser interactions, or other types of stored data. The educational logdata 201 may be aggregated (at 202) through knowledge engineered rulesand models 204 and be structured (at 202) according to one or more datamining rules and models 203. In one embodiment, the process 200 mayinclude tracking or conducting an assessment 210, using the data miningmodels 203, knowledge engineered rules 204 and educational log data 201.The process 200, after tracking or conducting an assessment 210 may sendeducational log data 201 back through the process 200 to again beaggregated (at 202). As illustrated in process 200, tracking orconducting an assessment 210 may check if a skill or skills have beenacquired 215. If the process determines (at 215) that a skill either hasor has not been acquired, the process may provide real-time feedback220. At the same time, the process can send educational log data 201back through the process to again be aggregated and/or structured (at202). The real-time feedback 220 may also send educational log data 201back through the process to again be aggregated and/or structured (at202), thus starting the process over. In one embodiment, the skillacquisition determination process may also return to receivesupplemental educational log data 201. In another embodiment, real-timefeedback 220 may be provided both when the student does not acquire theskill (including, but not limited to a specific help message on how toperform an inquiry skill/subskill), and when they do acquire the skill(including, but not limited to an acknowledgement of their successfulprogress).

FIG. 3 is a block diagram illustrating an example embodiment of aninquiry tutoring system according to the present invention. The inquirytutoring system 300 may include a simulation user interface 310, a helpsystem 340, a knowledge engineering based module 350, and data miningbased module 360. The simulated user interface 310 is one embodiment ofthe user interface 160 represented in the software architecture 100 ofFIG. 1. The knowledge engineering based module 350 and the data miningbased module 360 may be assessment models, tracking models, or both. Thehelp system 340 may be a pedagogical agent. Data generated from thesimulation UI 310 may be sent to either the knowledge-engineered module350 and/or the data mining module 360. The knowledge-engineered module350 and/or the data mining module 360 may also be in communication withthe help system 340. The simulation user interface 310 may include oneor more interface elements. These interface elements may include anavigation or phase bar illustrating state or stages of science inquiry.In one embodiment, a phase bar may include an explore phase 321, ahypotheses phase 322, an experiment phase 323, an analyze phase 324, acommunicate findings phase 327, and/or other phases. Phases may be addedor removed as needed. Within this environment, a user may generatehypotheses, collect data to test hypotheses, interpret data, warrantclaims with data, and communicate findings.

As illustrated in FIG. 3, the simulation user interface 310 may alsoinclude a goal interface element 325 and a Myhypothesis interfaceelement 326. The goal 325 and/or Myhypothesis interface 326 elements mayreceive input from one or more users or display output regarding astatus within the simulation environment. For example, the goalinterface element may display a specific goal the system wants the userto reach. In one embodiment, the Myhypothesis element 326 may allow auser to submit input regarding a hypothesis derived from collecting dataand interpreting the data from the explore phase 321. The simulationuser-interface 310 may also include a data collection frame 312, a modelvisualization frame 314, a data visualization frame 316, an analysisframe 318, and a findings (or word processing) frame 319. The modelvisualization frame 314 may include, but are not limited to, asimulation, a microworld, or an animation, including but not limited toa flash or HTML5/CSS/JavaScript animation. Upon determination from thepedagogical agent and/or help system 340 that the user desires orrequires feedback or assistance, one or more messages 329 may beprovided. The simulation user-interface 310 may include other types offrames, examples of which are seen in FIGS. 4A-4E.

FIGS. 4A-4E are example screenviews of the user-interface 310 from FIG.3. FIG. 4A is an example embodiment of the user interface in thehypothesis phase 322. The user reads a goal from the goal interfaceelement 325, and may input values into the Myhypothesis interface 326regarding the possible outcomes of the simulated experiment. The usermay then interact with the user interface by pressing the button 301 tocontinue to the next phase, the experiment phase 323.

FIG. 4B is an example embodiment of the user interface in experimentphase 323 (called “Collect Data”). The goal interface element 325remains the same as FIG. 4A. The values input in the Myhypothesisinterface 326 are present for the user to see during the experiment. Theuser may interact with data collection frame 312 by changing thevariables in the data collection frame 312, including, but not limitedto “Heat Amount,” “Ice Amount,” and “Container Size.” The user mayvisualize a simulation, model, animation, or microworld in the modelvisualization frame 314 based on the inputs from the data collectionframe 312. The data produced by the experiment is then visualized in thedata visualization frame 316. The results may be added to a resultsframe 317 that the user carries over to the next phase.

FIG. 4C is an example embodiment of the user interface in the analyzephase 324. The goal interface element 325 and Myhypothesis interface 326remain the same as the prior phase, and remain present for the user tosee. The analysis frame 318 may be manipulated by the user to highlightcertain data points. This allows the user to interact with the data,reach a conclusion based on the simulated experiment, select which databest serves as evidence to support their conclusion, and determinewhether their initial hypothesis was supported.

FIG. 4D is an example embodiment of the user interface in thecommunicate findings phase 327. The goal interface element 325 andMyhypothesis interface 326 remain the same as the prior phase, andremain present for the user to see. The analysis frame 318, with theinputs from the previous phase, is also present for the user to see. Inthis example, the user may interact with a word-processing frame 319 toallow the user to create a written description of the experiments andresults that the user obtained during the simulation.

FIG. 4E is an example embodiment of the user interface displaying amessage provided by the pedagogical agent and/or help system 340. Thisscreenview is analogous to the data collection frame 312 of FIG. 4B. Inthis example embodiment, the user may still interact with the datacollection frame 312 and view the Myhypothesis interface 326 establishedearlier. Upon determination from the pedagogical agent and/or helpsystem 340 that the user desires or requires feedback or assistance, amessage 329 may be provided.

Referring back to FIG. 3, assessment for simulation-based inquirylearning may include models developed through (1) knowledge engineeringand/or cognitive task analysis, and (2) models developed through datamining and/or machine learning methods. In knowledge engineering and/orcognitive task analysis approaches, rules 350 are defined a priori andencapsulate specific behaviors or differing levels of systematicexperimentation and/or inquiry skill. For example, knowledge engineeredrules may include a rule-based Adaptive Control of Thought-Rational(ACT-R) model of scientific inquiry based on an assessment of skilldifferences between experts and novices on formulating hypotheses,exploring, analyzing data, and generating conclusions. The ACT-R modelmay be used to model overt, observable human behavior(s). The rule basedescribes cognition as involving declarative knowledge (i.e., knowledgeabout things), and procedural knowledge (i.e., skills that act onknowledge); procedural knowledge is implemented in ACT-R models asproduction rules.

With ACT-R in mind, knowledge-engineering models may be leveraged usinga method called model-tracing, where student responses are matched to aknowledge-engineered cognitive model of expert and/or correct behaviorthat includes declarative knowledge and production rules and, in somecases, specific misconceptions, including, but not limited to bugs ordefects. In one embodiment, model tracing may be used with productionrules to auto-score students' inquiry on the use of thecontrol-of-variables (CVS) strategy and more broadly on designingcontrolled experiments, where all but the target variable is changedacross trials within a simulation, such as a science microworld. See forexample, the following publications that are hereby incorporated byreference: Sao Pedro, M. A., Real-Time Assessment, Prediction, andScaffolding of Middle School Students' Data Collection Skills withinPhysical Science Microworlds, Social Science and Policy Studies:Learning Sciences and Technologies Program Ph.D. Dissertation, WorcesterPolytechnic Institute, (April 2013); Gobert, J. D., Sao Pedro, M. A.,Baker, S. J. d., Toto, E., and Montalvo, O., Leveraging educational datamining for real time performance assessment of scientific inquiry skillswithin microworlds, Journal of Educational Data Mining, 4, 1 (2012),111-143.

Knowledge-engineered models may also be leveraged to track studentproficiency at inquiry skills over time and across science topics. Forexample, a rational model may average students' performance over time atan inquiry skill/subskill as an estimate, i.e., a measure or evaluationof their proficiency. See for example, the following publication that ishereby incorporated by reference: Sao Pedro, M. A., Baker, Ryan S. J.d., Gobert, J. D., Montalvo, O., and Nakama, A. LeveragingMachine-Learned Detectors of Systematic Inquiry Behavior to Estimate andPredict Transfer of Inquiry Skill. User Modeling and User-AdaptedInteraction (2013), 23(1), 1-39.

With respect to data mining based models 360, educational data miningand/or machine learning approaches may be employed and includediscovering student inquiry behaviors from data. For example, aself-organizing artificial neural network may build models of novice andexpert performance using transition logs within a given learningenvironment, for example, a high school chemistry class. These modelsmay be leveraged to construct a hidden Markov model for identifyinglearner trajectories through a series of activities.

Data mining approaches (at 360) may be used to distinguish students'problem solving strategies within exploratory learning environments. Forexample, clustering techniques and class association rules may capturelearner models of effective and ineffective learning strategies withinan exploratory learning environment for learning about a constraintsatisfaction algorithm. In one embodiment, a constraint satisfactionalgorithm may include identifying a constraint, setting a threshold forsatisfying the constraint and/or determining whether the constraintsatisfaction has been met. In another embodiment, a decision tree withcutoff values for certain features may be used as an assessment model toevaluate whether a student has demonstrated an inquiry subskill. See forexample, the following publications that are hereby incorporated byreference: Sao Pedro, M. A., Baker, Ryan S. J. d., Gobert, J. D.,Montalvo, O., and Nakama, A. Leveraging Machine-Learned Detectors ofSystematic Inquiry Behavior to Estimate and Predict Transfer of InquirySkill. User Modeling and User-Adapted Interaction (2013), 23(1), 1-39;Sao Pedro, M. A., Real-Time Assessment, Prediction, and Scaffolding ofMiddle School Students' Data Collection Skills within Physical ScienceSimulations, Social Science and Policy Studies: Learning Sciences andTechnologies Program Ph.D. Dissertation, Worcester PolytechnicInstitute, (April 2013). Data mining models 360 may also be used with atask-dependent and/or a task-independent machine-learned model topredict skill proficiency in computer desktop applications.

Data mining approaches (at 360) may also be used to track studentinquiry skill and sub skill proficiency development over time and acrosssimulation topics/domains. For example, Model-tracing assessments andother approaches may be, in turn, utilized within knowledge-tracing. Inone embodiment, knowledge tracing may include assessing latent knowledgefrom correct and incorrect performance. Knowledge-tracing models may beimplemented as a form of Bayesian Networks and/or Bayes nets (BNs). Forexample, BNs may assess procedural knowledge for physics within variouslearning environments. A dynamic BN may model middle school students'narrative, strategic, and curricular knowledge as students explore athree-dimensional (3D) immersive environment on microbiology. In oneembodiment, BN's may include or utilize related diagnostic measurementtools to model multivariate skill profiles for network engineering basedon performance in an interactive digital learning environment. Inanother embodiment, data-mined assessment models may be utilized withinknowledge-tracing. See for example, the following publications that arehereby incorporated by reference: Sao Pedro, M. A., Baker, Ryan S. J.d., Gobert, J. D., Montalvo, O., and Nakama, A. LeveragingMachine-Learned Detectors of Systematic Inquiry Behavior to Estimate andPredict Transfer of Inquiry Skill. User Modeling and User-AdaptedInteraction (2013), 23(1), 1-39; Sao Pedro, M. A., Baker, Ryan S. J. d.,Gobert, J. D., Incorporating Scaffolding and Tutor Context into BayesianKnowledge Tracing to Predict Inquiry Skill Acquisition. In S. K.D'Mello, R. A. Calvo, & A. Olney (Eds.) Proceedings of the 6thInternational Conference on Educational Data Mining, (pp. 185-192).Memphis, Tenn.

FIG. 5 is a block diagram of a computer system according to oneembodiment of an Inquiry Assessment Platform 400 (“IA Platform”). Thiscomputer system may reside either on the client side, the server side,or some combination thereof. In this embodiment, the IA Platform mayserve to aggregate, process, store, search, serve, identify, instruct,generate, match, and/or facilitate tutoring interactions with acomputer. Aggregated data may be stored for later retrieval, analysis,and manipulation, which may be facilitated through a database program437, or one or more computer-implemented tables 450 (collectively, 450a, 450 b, 450 c, 450 d, 450 e, 450 f, 450 g, and 450 h in FIG. 5).

In one embodiment, the IA Platform 400 may be connected to and/orcommunicate with entities such as, but not limited to: one or more usersfrom user input devices (e.g., Flash/SD/SSD); peripheral devices, e.g.,a simulation environment; an optional cryptographic processor device;and/or a communications network 420. Networks are commonly thought tocomprise the interconnection and interoperation of clients, servers, andintermediary nodes in a graph topology. It should be noted that the term“server” as used throughout this application refers generally to acomputer, other device, program, or combination thereof that processesand responds to the requests of remote users across a communicationsnetwork. Servers 439 may serve their information to requesting“client(s)”. The term “client” as used herein refers generally to acomputer, program, other device, user and/or combination thereof that iscapable of processing and making requests and obtaining and processingany responses from servers across a communications network. Variousclient-server architecture and configurations are suitable, as well asother than a client-server architecture is suitable. For example, aweb-based system may be utilized to implement the present invention aswell as a monolithic (running on one machine) or semi-monolithic system(e.g., installed and running on a tablet that send data to a server).

The processor and/or transceivers may be connected as either internaland/or external peripheral devices (e.g., sensors 456) via the I/O ports455. In turn, the transceivers may be connected to antenna(s) 457,thereby effectuating wireless transmission and reception of variouscommunication and/or sensor protocols. The CPU 451 comprises at leastone high-speed data processor adequate to execute program components forexecuting user and/or system-generated requests. Embedded components mayinclude software solutions, hardware solutions, and/or some combinationof both hardware/software solutions. Storage interfaces, e.g., datastore 431, may accept, communicate, and/or connect to a number ofstorage devices such as, but not limited to: storage devices, removabledisc devices, solid state drives (SSD) and/or the like. Storageinterfaces may employ connection protocols such as, but not limited to:(Ultra) (Serial) Advanced Technology Attachment (Packet Interface)((Ultra) (Serial) ATA(PI)), (Enhanced) Integrated Drive Electronics((E)IDE), Institute of Electrical and Electronics Engineers (IEEE) 1394,fiber channel, Small Computer Systems Interface (SCSI), Universal SerialBus (USB), and/or the like.

Network card(s) may accept, communicate, and/or connect to acommunications network 420. Through a communications network 420, the IAPlatform is accessible through remote clients (e.g., computers with webbrowsers) by users. Network interfaces 454 may employ connectionprotocols such as, but not limited to: direct connect, Ethernet (thick,thin, twisted pair 10/100/1000 Base T, and/or the like), Token Ring,wireless connection such as IEEE 802.11a-x, and/or the like. A cloudservice 425 may be in communication with the IA Platform. The cloudservice 425 may include a Platform-as-a-Service (PaaS) model layer 425a, an Infrastructure-as-a-Service (IaaS) model layer 425 b and aSoftware-as-a-Service (SaaS) model layer 425 c. The SaaS model layer 425c generally includes software managed and updated by a central location,deployed over the Internet and provided through an access portal. ThePaaS model layer 425 a generally provides services to develop, test,deploy, host and maintain applications in an integrated developmentenvironment. The IaaS layer model layer 425 b generally includesvirtualization, virtual machines, e.g., virtual servers, virtualdesktops and/or the like.

Input Output interfaces (I/O) 455 may accept, communicate, and/orconnect to user input devices, peripheral devices, cryptographicprocessor devices, and/or the like. Peripheral devices may be connectedand/or communicate to I/O and/or other facilities of the like such asnetwork interfaces, storage interfaces, directly to the interface bus,system bus 458, the CPU 451, and/or the like. Peripheral devices may beexternal, internal, and/or part of IA Platform. Peripheral devices mayinclude: eye tracking equipment, antenna 457, audio devices (e.g.,line-in, line-out, microphone input, speakers, etc.), cameras (e.g.,still, video, webcam, etc.), dongles (e.g., for copy protection,ensuring secure transactions with a digital signature, and/or the like),external processors (for added capabilities; e.g., crypto devices),force-feedback devices (e.g., vibrating motors), network interfaces 454,printers, scanners, storage devices, transceivers (e.g., cellular, GPS,etc.), video devices (e.g., goggles, monitors, etc.), video sources,visors, and/or the like. Peripheral devices often include types of inputdevices (e.g., cameras).

Generally, any mechanization and/or embodiment allowing a processor toaffect the storage and/or retrieval of information is regarded asmemory. It is to be understood that the IA Platform and/or a computersystems may employ various forms of memory. In a typical configuration,memory includes ROM/RAM 452, a cache 453, and a storage device. Astorage device may be any conventional computer system storage. Storagedevices may include a (fixed and/or removable) magnetic disk drive; amagneto-optical drive; an optical drive; an array of devices (e.g.,Redundant Array of Independent Disks (RAID)); solid state memory devices(USB memory, solid state drives (SSD), etc.); other processor-readablestorage mediums; and/or other devices of the like. Thus, a computersystem 403 generally requires and makes use of non-transitory and/ortransitory memory.

A user interface component 441 is a stored program component that isexecuted by a CPU 451. The user interface 441 may be a graphical userinterface such as simulation user interface 310 and provided by, with,and/or atop operating systems 433 and/or operating environments. Theuser interface may allow for the display, execution, interaction,manipulation, and/or operation of program components and/or systemfacilities through textual and/or graphical facilities. The userinterface 441 provides a facility through which users may affect,interact, and/or operate a computer system 403. A user interface 441 maycommunicate to and/or with one or more other components 435(collectively, 435 a, 435 b, 435 c, and 435 d in FIG. 4) in a componentcollection, including itself, and/or facilities of the like.

The structure and/or operation of any of the IA Platform engine set 405may be combined, consolidated, and/or distributed in any number of waysto facilitate development and/or deployment. Similarly, the componentcollection may be combined in any number of ways to facilitatedeployment and/or development. To accomplish this, one may integrate thecomponents into a common code base or in a facility that may dynamicallyload the components on demand in an integrated fashion. The Engine Set405 components may be consolidated and/or distributed in countlessvariations through standard data processing and/or developmenttechniques. Multiple instances of any one of the program components inthe program component collection 435 may be instantiated on a singlenode, and/or across numerous nodes to improve performance throughload-balancing and/or data-processing techniques. Furthermore, singleinstances may also be distributed across multiple controllers and/orstorage devices; e.g., databases. All program component instances andcontrollers working in concert may do so through standard dataprocessing communication techniques. The component collection 435 may becomponents for implementing system 100 or system 300 described above inFIGS. 1 and 3, respectively.

The configuration of the IA Platform depends on the context of systemdeployment. Factors such as, but not limited to, the budget, capacity,location, and/or use of the underlying hardware resources may affectdeployment requirements and configuration. Regardless of whether theconfiguration results in more consolidated and/or integrated programcomponents, results in a more distributed series of program components,and/or results in some combination between a consolidated anddistributed configuration, data may be communicated, obtained, and/orprovided. Instances of components consolidated into a common code basefrom the program component collection may communicate, obtain, and/orprovide data. This may be accomplished through intra-application dataprocessing communication techniques such as, but not limited to: datareferencing (e.g., pointers), internal messaging, object instancevariable communication, shared memory space, variable passing, and/orthe like.

In certain embodiments, the procedures, devices, and processes describedherein constitute a computer program product, including a computerreadable medium, e.g., a removable storage medium such as one or moreDVD-ROM's, CD-ROM's, diskettes, tapes, etc., that provides at least aportion of the software instructions for the system. Such a computerprogram product may be installed by any suitable software installationprocedure, as is well known in the art. In another embodiment, at leasta portion of the software instructions may also be downloaded over acable, communication and/or wireless connection.

Embodiments may also be implemented as instructions stored on anon-transitory machine-readable medium, which may be read and executedby one or more processors. A non-transient machine-readable medium mayinclude any mechanism for storing or transmitting information in a formreadable by a machine, e.g., a computing device 403. For example, anon-transient machine-readable medium may include read only memory(ROM); random access memory (RAM); magnetic disk storage media; opticalstorage media; flash memory devices; and others.

It should be understood that the example embodiments described above maybe implemented in many different ways. In some instances, the variousmethods and machines described herein may be implemented by a physical,virtual, or hybrid general-purpose computer, or a computer networkenvironment such as the computer network environment 420. A generalpurpose computer may be transformed into the machines that execute themethods described above, for example, by loading software instructionsinto memory or nonvolatile storage for execution by a central processingunit.

Embodiments or aspects thereof may be implemented in the form ofhardware, firmware, or software or any combination thereof. Ifimplemented in software, the software may be stored on any non-transientcomputer readable medium that is configured to enable a processor toload the software or subsets of instructions thereof. The processor thenexecutes the instructions and is configured to operate or cause anapparatus to operate in a manner as described herein.

Further, firmware, software, routines, or instructions may be describedherein as performing certain actions and/or functions of dataprocessors. However, it should be appreciated that such descriptionscontained herein are merely for convenience and that such actions infact result from computing devices, processors, controllers, or otherdevices executing the firmware, software, routines, instructions, etc.

It also should be understood that the flow diagrams, block diagrams, andnetwork diagrams may include more or fewer elements, be arrangeddifferently, or be represented differently. But it further should beunderstood that certain implementations may dictate the block andnetwork diagrams and the number of block and network diagramsillustrating the execution of the embodiments be implemented in aparticular way.

Accordingly, further embodiments may also be implemented in a variety ofcomputer architectures, physical, virtual, cloud computers, one or moreservers, one or more clients, and/or some combination thereof, and,thus, the data processors described herein are intended for purposes ofillustration only and not as a limitation of the embodiments.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

For example, further details of other embodiments may be found in atleast the following three publications, that are hereby incorporated byreference: (1) Sao Pedro, M. A., Baker, R. S. J. d., and Gobert, J. D.(2012), Improving Construct Validity Yields Better Models of SystematicInquiry, Even with Less Information, In Proceedings of the 20thConference on User Modeling, Adaptation, and Personalization (Montreal,QC, Canada 2012), 249-260; (2) Sao Pedro, M. A., Baker, R. S. J. d.,Gobert, J. D., Montalvo, O., and Nakama, A., Leveraging Machine-LearnedDetectors of Systematic Inquiry Behavior to Estimate and PredictTransfer of Inquiry Skill, User Modeling and User-Adapted Interaction(2013), 23(1), 1-39; (3) Gobert, J. D., Sao Pedro, M. A., Baker, S. J.d., Toto, E., and Montalvo, O., Leveraging educational data mining forreal time performance assessment of scientific inquiry skills withinmicroworlds, Journal of Educational Data Mining, 4, 1 (2012), 111-143;Gobert, J. D., Sao Pedro, M. A., Raziuddin, J., Baker, R. S., From LogFiles to Assessment Metrics: Measuring Students' Science Inquiry SkillsUsing Educational Data Mining, Journal of the Learning Sciences, 22:521-563 (2013).

What is claimed is:
 1. A method, comprising: in a computer, collectinginformation from a subject person; and in the computer, providing areal-time communication indicating whether one or more sciencecompetencies of the subject person as assessed within a virtualenvironment match the collected information of the subject person, whenthe subject person performs at least one of: generating hypotheses;collecting data to test the hypotheses; interpreting the collected data;warranting claims with data; and communicating respective findings. 2.The method of claim 1, further comprising a tracking model that tracksthe one or more science assessed competencies of the subject personacross one or more science topics or science domains including at leastone physics, physical sciences, chemistry, life sciences, geosciences,biological sciences, nursing, medicine, medical sciences, physiologicalsciences, natural sciences, formal sciences, social sciences, technicaldomains, vocational domains, computer sciences, computational thinking,engineering, electronics, and troubleshooting.
 3. The method of claim 1,further comprising generating a real-time communication to anotherperson based upon measuring of the one or more science competencies, andresponsive to the real-time communication, the another person providingat least a portion of the real-time feedback to the subject person. 4.The method of claim 1, wherein the one or more science competenciesinclude at least one of, or any subskill of: a data collection skill, adata interpretation skill, a hypothesis skill, a question-asking skill,a claim warranting skill, a communicating findings skill, acommunicating explanations skill, an argumentation skill, an identifyingan independent variable skill, an identifying a dependent variableskill, a defining a relationship between variables skill, a designing acontrolled experiment skill, a testing a stated hypothesis skill, awarranting an interpretation skill, a relating an interpretation to ahypothesis skill, reasoning, critical thinking, and problem solving. 5.The method of claim 1, further comprising: defining, by the computer,one or more measurable science competencies, in a computer; measuringthe one or more science competencies of the subject person, themeasuring being in real-time and using at least one of an assessmentmodel and a tracking model programmed to infer science competencydemonstration from interactive engagement by the subject person with thevirtual environment; and providing to the subject person real-timefeedback through the virtual environment.
 6. The method of claim 1,further comprising providing the real-time communication to anotherperson, the real-time communication including at least one of: an alertassociated with the one or more science competencies of the subjectperson; and an assessment of the one or more science competencies of thesubject person.
 7. The method of claim 1, further comprising providingthe real-time communication to the subject person, the real-timecommunication including tutoring of the one or more science competenciesfor the subject person.
 8. The method of claim 1, further comprising:providing a real-time assessment of the one or more science competenciesof the subject person based upon data measured from an eye trackingsystem monitoring the subject person; and providing real-timecommunication indicating whether the subject person is attending to oneor more aspects of at least one of a text or diagram associated with thevirtual environment based upon the subject person's understanding of atarget domain and based upon the real-time assessment.
 9. The method ofclaim 1, wherein: generating hypotheses includes forming and askingquestions and defining problems; collecting data to test the hypothesisincludes planning and carrying out investigations using models;interpreting the collected data includes analyzing the collected data;warranting claims with data includes engaging in arguments fromevidence; and communicating respective findings includes generatingexplanations from the collected data and designing solutions from thecollected data.
 10. The method of claim 1, further comprising providingat least one of: a real-time assessment of the one or more sciencecompetencies of the subject person based upon at least one of a patternof the subject person and a decision of the subject person; and any of acertification for licensure, a certification for a course, and acertification for a lesson.
 11. A system, comprising: a computing moduleconfigured to collect information from a subject person; and a userinterface module configured to provide a real-time communicationindicating whether one or more science competencies of the subjectperson as assessed within a virtual environment match the informationcollected from the subject person, when the subject person performs atleast one of: generating hypotheses; collecting data to test thehypotheses; interpreting the collected data; warranting claims withdata; and communicating respective findings.
 12. The system of claim 11,wherein the user interface module is further configured to generate areal-time communication to another person based upon at least one ofmeasurement and tracking of the one or more science competencies by thecomputing module, and responsive to the real-time communication, theanother person providing at least a portion of the real-time feedback tothe subject person.
 13. The system of claim 11, wherein the one or morescience competencies include at least one of, or any subskill of: a datacollection skill, a data interpretation skill, a hypothesis skill, aquestion-asking skill, a claim warranting skill, a communicatingfindings skill, a communicating explanations skill, an argumentationskill, an identifying an independent variable skill, an identifying adependent variable skill, a defining a relationship between variablesskill, a designing a controlled experiment skill, a testing a statedhypothesis skill, a warranting an interpretation skill, a relating aninterpretation to a hypothesis skill, reasoning, critical thinking, andproblem solving.
 14. The system of claim 11, further comprising: aprocessing module configured to define one or more measurable sciencecompetencies; the computing module being configured to perform at leastone of measurement and tracking, in real-time, of the one or morescience competencies of the subject person and to use at least one of anassessment model and a tracking model programmed to infer sciencecompetency demonstration from interactive engagement by the subjectperson with the virtual environment; and the user interface module beingfurther configured to provide to the subject person real-time feedbackthrough the virtual environment.
 15. The system of claim 11, wherein theuser interface module is further configured to provide the real-timecommunication to another person, the real-time communication includingat least one of: an alert associated with the one or more sciencecompetencies of the subject person; and an assessment of the one or morescience competencies of the subject person.
 16. The system of claim 11,wherein the user interface module is further configured to provide thereal-time communication to the subject person, the real-timecommunication including tutoring of the one or more science competenciesfor the subject person.
 17. The system of claim 11, wherein the userinterface module is further configured to: provide a real-timeassessment of the one or more science competencies of the subject personbased upon data measured from an eye tracking system monitoring thesubject person; and provide real-time communication indicating whetherthe subject person is attending to one or more aspects of at least oneof a text or diagram associated with the environment based upon thesubject person's understanding of a target domain and based upon thereal-time assessment.
 18. The system of claim 11, wherein: generatinghypotheses includes forming and asking questions and defining problems;collecting data to test the hypothesis includes planning and carryingout investigations using models; interpreting the collected dataincludes analyzing the collected data; warranting claims with dataincludes engaging in arguments from evidence; and communicatingrespective findings includes generating explanations from the collecteddata and designing solutions from the collected data.
 19. The system ofclaim 11, wherein the user interface module is further configured toprovide at least one of: a real-time assessment of the one or morescience competencies of the subject person based upon at least one of apattern of the subject person and a decision of the subject person; andany of a certification for licensure, a certification for a course, anda certification for a lesson.
 20. A computer program product providing atutoring agent comprising: a non-transitory computer useable mediumhaving a computer readable program; wherein the computer readableprogram when executed on a computer causes the computer to: collectinformation from a subject person; and provide a real-time communicationindicating whether one or more science competencies of the subjectperson as assessed within a virtual environment match the informationcollected from the subject person, when the subject person performs atleast one of: generating hypotheses; collecting data to test thehypotheses; interpreting the collected data; warranting claims withdata; and communicating respective findings.